// ARS TECHNICA — INTELLIGENZA ARTIFICIALE
Researchers use AI to design proteins that cut up other proteins
AI design tools make proteins that break bonds that are stable for hundreds of years.
A protein is a chain of amino acids held together by what are called peptide bonds. On their own, those bonds are extremely stable; they’re estimated to have a half-life of several hundred years. But in Earth’s environment, most proteins don’t last nearly that long. That’s because organisms have evolved a handful of ways to catalyze the break-up of peptide bonds, which have diversified into countless specialized protein-digesting enzymes. Proteases are everywhere and generally make short work of any proteins that find their way into the environment.
Now, researchers have basically short-circuited the diversification process, using AI-based protein design tools to take the catalytic core of two types of protease and replace the entire rest of the protein. The resulting enzymes could be tuned to cut specific proteins in half, and the researchers demonstrated their effectiveness by using them to cut proteins associated with neurological diseases.
The basic peptide bond isn’t especially interesting-looking—it’s what you get when you link an amino group to an acid, releasing a water molecule in the process. As their name implies, amino acids have both an amino and an acid group, allowing them to form peptide bonds on two sides of the amino acid. This is critical for building up the long chains found in proteins. (The amino acids also have side chains with distinctive chemistry, which gives a backbone specific properties.)
Breaking these bonds apart simply reverses the process by breaking apart the peptide bond and a water molecule, and linking their parts. Because both components are chemically stable, the reaction requires an enzyme to occur with any frequency. Without an enzyme, a typical peptide bond would be stable for hundreds of years in water.
Life has evolved a handful of enzyme mechanisms that manage this catalysis. The research at issue here relied on two different ones. One is a zinc protease, where the positive charge of the zinc atom coordinates the peptide bond that’s being broken, key parts of the protease, and a water molecule. The protease partially pulls a proton off the water molecule, activating it to attack and break up the peptide bond.
The second protease the researchers use is called a cysteine protease. In this case, a cysteine (one of the amino acids) in the enzyme is activated to attack the peptide bond, creating a temporary intermediate where part of the protein being cut is chemically linked to the enzyme. A water molecule eventually breaks this intermediate apart, releasing the second part of the cut protein.
Both of these mechanisms require a handful of specific amino acids from the protease to be located in close proximity within the catalytic center of the protease. Other amino acids in the protease help ensure these key catalytic amino acids are brought together in the right place with the right orientation. The rest of the protease may also enforce a degree of specificity on what proteins get cut. Some proteases, like the ones active in our digestive system, can make cuts next to any amino acid. Others are slightly more selective, preferring to cut near positively or negatively charged amino acids. And a few are very highly specific, such as the protease that activates insulin, which only cuts near a very specific series of amino acids.
The people behind the new work, primarily from Nobel-winner David Baker’s lab at the University of Washington, viewed designing a protease as a challenge. For one, the stability of the peptide bonds means that reactions wouldn’t take place without an effective catalyst. And to get that catalyst requires an extremely precise arrangement of specific amino acids. So it wasn’t obvious that the AI protein design tools the lab developed were up to the task.
Both projects took a similar approach. Just like evolution, they started with an existing protease. One was a zinc proteas